LapGM: A Multisequence MR Bias Correction and Normalization Model
This addresses MRI preprocessing challenges for medical imaging practitioners, but appears incremental as it builds on existing Gaussian mixture and regularization approaches.
The authors tackled bias field correction and intensity normalization for multi-sequence MRI by proposing LapGM, a spatially regularized Gaussian mixture model that allows control over bias removal versus contrast preservation. They compared LapGM to existing methods like N4ITK and various normalization techniques, though no concrete performance numbers were provided in the abstract.
A spatially regularized Gaussian mixture model, LapGM, is proposed for the bias field correction and magnetic resonance normalization problem. The proposed spatial regularizer gives practitioners fine-tuned control between balancing bias field removal and preserving image contrast preservation for multi-sequence, magnetic resonance images. The fitted Gaussian parameters of LapGM serve as control values which can be used to normalize image intensities across different patient scans. LapGM is compared to well-known debiasing algorithm N4ITK in both the single and multi-sequence setting. As a normalization procedure, LapGM is compared to known techniques such as: max normalization, Z-score normalization, and a water-masked region-of-interest normalization. Lastly a CUDA-accelerated Python package $\texttt{lapgm}$ is provided from the authors for use.